- Standardised vs raw effects: added
`incl.raw`

argument to`stdEff()`

(`stdCoeff()`

), to append raw effects (unstandardised coefficients) to the output. This facilitates simultaneous bootstrapping of both sets of effects, allowing raw effects to be used alternatively for calculating (`semEff(..., use.raw = TRUE)`

) or predicting (`predEff(..., use.raw = TRUE)`

) effects/CIs.

- Renamed function
`stdCoeff()`

to`stdEff()`

, to better reflect the concept of standardised model coefficients as ‘effects’ (calling`stdCoeff()`

will still work - with a warning - until the next version at least). - Added
`offset`

argument to`getY()`

and`R2()`

, to explicitly retain/remove an offset (where present) in/from the response variable or fitted values. Offsets are removed by default, which ensures, for example, that standardised effects are scaled appropriately. - Added
`env`

argument to multiple functions, for explicitly specifying the location of data used to fit models (not necessary in most circumstances). This replaces the`...`

argument in many instances, which was previously used to pass an environment to`eval()`

(via`getData()`

).`env`

(and`data`

) can also now be passed (`...`

) to`bootEff()`

and`predEff()`

. - Added confidence interval attributes to
`bootCI()`

/`semEff()`

output (i.e. confidence level, type). `R2()`

no longer calculates predictive R-squared for GLMMs, as the interpretation of the hat matrix used in calculations is not reliable (see https://rdrr.io/cran/lme4/man/hatvalues.merMod.html).- Removed ability to pass arguments from
`getY()`

to`glt()`

, allowing more controlled output of`getY(..., link = TRUE)`

. - Various minor updates to function code and documentation, improvement and addition of some new internal helper functions.

`bootEff()`

specified with correlated errors failed for mixed models of class`"lmerModLmerTest"`

(issue with re-fitting models using`update()`

).`predEff()`

failed to evaluate some complex model terms (e.g. polynomials).`stdEff()`

(`stdCoeff()`

) did not re-fit model properly to calculate correct VIFs for a fully ‘centred’ model (i.e. did not account sufficiently for complex terms such as polynomials or transformations, where mean-centring should occur as the final step).`xNam()`

generated incorrect term names for categorical predictors under certain circumstances (different contrast types, interactive effects with no ‘main’ effects).`stdEff()`

(`stdCoeff()`

) incorrectly calculated ‘centred’ intercept for models with an offset specified.`predEff()`

failed when a nested list of models and list of numeric weights were supplied (i.e. a model averaging scenario).`stdEff()`

(`stdCoeff()`

) did not return the ‘phi’ parameter(s) for beta regression models.

- Support for mixed models of class
`"lmerModLmerTest"`

. - New function
`glt()`

, for calculating ‘generalised’ link transformations for non-gaussian variables.

- Transfer of some functionality from
`getY()`

to`glt()`

. - Minor changes to arguments in
`bootEff()`

and`getY()`

. - Added ability in
`stdCoeff()`

to use variables not present in the model design matrix (e.g. a ‘missing’ main effect for an interaction). - Added ability to pass a boot object (from
`bootEff()`

) to the`effects`

argument of`predEff()`

. - Added a
`refit.x`

argument to`stdCoeff()`

, allowing control over whether to refit the model with centred predictors (for correct VIFs). - Various updates to documentation.

`xNam()`

did not generate correct term names for categorical variables with contrast types other than`contr.treatment()`

.`stdCoeff()`

did not correctly adjust for multicollinearity for a model containing categorical variables when centring was specified (`cen.x = TRUE`

).`getY()`

failed to generate an estimated working response when a variable with missing values (`NA`

) was supplied (this functionality now in`glt()`

).`predEff()`

failed for models with categorical variables (did not access dummy variables in model matrix).

- Function
`semEff()`

did not output effects properly.

- Added support for generalised least squares models (class
`"gls"`

). - Added support for beta regression models (class
`"betareg"`

).

- Function
`xNam()`

did not generate correct term names for interactions involving multi-coefficient terms (e.g. factors). - Function
`xNam()`

did not generate correct term names for factors when the model intercept is suppressed. - Function
`R2()`

with argument`pred = TRUE`

threw an error for models where any weights = 0.

New package `semEff`

, allowing the automatic calculation of effects for ‘piecewise’ structural equation models.